{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2587,
     "status": "ok",
     "timestamp": 1597492072008,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "5NCGpNCVotjd"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "plt.style.use('fivethirtyeight')\n",
    "np.random.seed(100)\n",
    "#import os\n",
    "#file_dir=os.getcwd()#获取当前工作目录路径\n",
    "save_model='lstm.h5'\n",
    "window=3#时间窗\n",
    "scale=2000#归一化参数\n",
    "amount_of_features=1#特征数量\n",
    "d=0.05#dropout系数\n",
    "batch_size=16#批训练数量\n",
    "epoch=100#总迭代次数\n",
    "train_file='alic90_131.xls'#file_dir+'\\\\data\\\\alic\\\\alic90_131.xls'#读取训练集地址\n",
    "test_file='alic132_136.xls'#file_dir+'\\\\data\\\\alic\\\\alic132_136.xls'#读取测试集地址\n",
    "save_file='alic_predict_lstm.csv'#file_dir+'\\\\data\\\\alic\\\\alic_predict_lstm.csv'#储存预测值地址"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2580,
     "status": "ok",
     "timestamp": 1597492072008,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "u4Eyqsdaotjh"
   },
   "outputs": [],
   "source": [
    "#读取数据，构造训练集和测试集的函数\n",
    "def read_train(file_name,window,scale):\n",
    "    #读取数据 \n",
    "    data=pd.read_excel(file_name,header=None) \n",
    "    data=data[1]\n",
    "    #构造针对机器学习模型的数据集\n",
    "    window=window#时间窗\n",
    "    data=data.values \n",
    "    dataset=data\n",
    "    for i in range(window):\n",
    "        zero=np.zeros(i+1)\n",
    "        temp=np.append(data[i+1:],zero)\n",
    "        dataset=np.row_stack((dataset,temp))\n",
    "    dataset=pd.DataFrame(dataset).T\n",
    "    dataset=dataset.iloc[:-window]\n",
    "    dataset=dataset/scale#进行伪归一化\n",
    "    #划分特征与标签\n",
    "    x=dataset.iloc[:,:-1]\n",
    "    y=dataset.iloc[:,-1]\n",
    "    return x,y\n",
    "def read_test(file_name,window,scale):\n",
    "    #读取数据 \n",
    "    data=pd.read_excel(file_name,header=None) \n",
    "    data=data.iloc[:,:]\n",
    "    data=data[0]\n",
    "    #构造针对机器学习模型的数据集\n",
    "    window=window#时间窗\n",
    "    data=data.values \n",
    "    dataset=data\n",
    "    for i in range(window):\n",
    "        zero=np.zeros(i+1)\n",
    "        temp=np.append(data[i+1:],zero)\n",
    "        dataset=np.row_stack((dataset,temp))\n",
    "    dataset=pd.DataFrame(dataset).T\n",
    "    dataset=dataset.iloc[:-window]\n",
    "    dataset=dataset/scale#进行伪归一化\n",
    "    #划分特征与标签\n",
    "    x=dataset.iloc[:,:-1]\n",
    "    y=dataset.iloc[:,-1]\n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2576,
     "status": "ok",
     "timestamp": 1597492072008,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "UqmQarL8otjj",
    "outputId": "068dd905-8a38-40c6-c417-1407aca65c65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1005, 3)\n",
      "(117, 3)\n",
      "(1005,)\n",
      "(117,)\n"
     ]
    }
   ],
   "source": [
    "#构造训练集测试集\n",
    "X_train,y_train=read_train(train_file,window,scale)\n",
    "X_test, y_test=read_test(test_file,window,scale)\n",
    "X_train,X_test,y_train,y_test=X_train.values,X_test.values,y_train.values,y_test.values\n",
    "print(X_train.shape)#通过输出训练集测试集的大小来判断数据格式正确。\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 89
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2570,
     "status": "ok",
     "timestamp": 1597492072009,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "Z14xqX3gotjn",
    "outputId": "54248355-3e0a-4f2d-84e0-1adfaa2dca4c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1005, 3, 1)\n",
      "(117, 3, 1)\n",
      "(1005,)\n",
      "(117,)\n"
     ]
    }
   ],
   "source": [
    "#LSTM神经网络的数据集要稍微做一些修改\n",
    "X_train = np.reshape(X_train, (X_train.shape[0],X_train.shape[1], amount_of_features))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1], amount_of_features))  \n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3600,
     "status": "ok",
     "timestamp": 1597492073045,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "i0dUbIgGotjq",
    "outputId": "9a38af4d-6447-4c0c-c7b0-d6b70db24324"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From E:\\anoconda\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 32)                4352      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 132       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 4,489\n",
      "Trainable params: 4,489\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#建立LSTM模型 训练\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation,Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "#建立训练模型过程\n",
    "model = Sequential()#建立层次模型\n",
    "model.add(LSTM(32, input_shape=(window, amount_of_features), return_sequences=False))#建立LSTM层\n",
    "#model.add(Dropout(d))\n",
    "model.add(Dense(4,activation='relu'))   #建立全连接层  \n",
    "#model.add(Dropout(d))\n",
    "model.add(Dense(1,activation='relu'))\n",
    "model.compile(loss='mse',optimizer='adam',metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 22110,
     "status": "ok",
     "timestamp": 1597492091563,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "j1TvX44ootjs",
    "outputId": "9987a27f-6bcf-4bfb-d47f-b95c46501fe0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From E:\\anoconda\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 904 samples, validate on 101 samples\n",
      "Epoch 1/100\n",
      "904/904 [==============================] - 1s 634us/step - loss: 1.1007 - accuracy: 0.0000e+00 - val_loss: 0.7545 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/100\n",
      "904/904 [==============================] - 0s 133us/step - loss: 0.1907 - accuracy: 0.0000e+00 - val_loss: 0.0010 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/100\n",
      "904/904 [==============================] - 0s 132us/step - loss: 3.8322e-04 - accuracy: 0.0000e+00 - val_loss: 9.5228e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/100\n",
      "904/904 [==============================] - 0s 136us/step - loss: 1.6540e-04 - accuracy: 0.0000e+00 - val_loss: 9.8899e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 5/100\n",
      "904/904 [==============================] - 0s 139us/step - loss: 1.6550e-04 - accuracy: 0.0000e+00 - val_loss: 9.4305e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 6/100\n",
      "904/904 [==============================] - 0s 125us/step - loss: 1.6538e-04 - accuracy: 0.0000e+00 - val_loss: 9.5148e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 7/100\n",
      "904/904 [==============================] - 0s 104us/step - loss: 1.6460e-04 - accuracy: 0.0000e+00 - val_loss: 9.6270e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 8/100\n",
      "904/904 [==============================] - 0s 127us/step - loss: 1.6420e-04 - accuracy: 0.0000e+00 - val_loss: 9.5039e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 9/100\n",
      "904/904 [==============================] - 0s 135us/step - loss: 1.6341e-04 - accuracy: 0.0000e+00 - val_loss: 9.6500e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 10/100\n",
      "904/904 [==============================] - 0s 145us/step - loss: 1.6454e-04 - accuracy: 0.0000e+00 - val_loss: 9.5122e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 11/100\n",
      "904/904 [==============================] - 0s 132us/step - loss: 1.6313e-04 - accuracy: 0.0000e+00 - val_loss: 9.2616e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 12/100\n",
      "904/904 [==============================] - 0s 141us/step - loss: 1.6399e-04 - accuracy: 0.0000e+00 - val_loss: 9.6195e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 13/100\n",
      "904/904 [==============================] - 0s 148us/step - loss: 1.6301e-04 - accuracy: 0.0000e+00 - val_loss: 8.8977e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 14/100\n",
      "904/904 [==============================] - 0s 140us/step - loss: 1.6172e-04 - accuracy: 0.0000e+00 - val_loss: 9.2349e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 15/100\n",
      "904/904 [==============================] - 0s 132us/step - loss: 1.5980e-04 - accuracy: 0.0000e+00 - val_loss: 8.8645e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 16/100\n",
      "904/904 [==============================] - 0s 140us/step - loss: 1.6327e-04 - accuracy: 0.0000e+00 - val_loss: 9.1008e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 17/100\n",
      "904/904 [==============================] - 0s 145us/step - loss: 1.5788e-04 - accuracy: 0.0000e+00 - val_loss: 8.6517e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 18/100\n",
      "904/904 [==============================] - 0s 161us/step - loss: 1.5692e-04 - accuracy: 0.0000e+00 - val_loss: 8.9109e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 19/100\n",
      "904/904 [==============================] - ETA: 0s - loss: 1.6175e-04 - accuracy: 0.0000e+ - 0s 145us/step - loss: 1.6142e-04 - accuracy: 0.0000e+00 - val_loss: 8.8561e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 20/100\n",
      "904/904 [==============================] - 0s 142us/step - loss: 1.5863e-04 - accuracy: 0.0000e+00 - val_loss: 8.7962e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 21/100\n",
      "904/904 [==============================] - 0s 137us/step - loss: 1.5572e-04 - accuracy: 0.0000e+00 - val_loss: 8.6405e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 22/100\n",
      "904/904 [==============================] - 0s 125us/step - loss: 1.5535e-04 - accuracy: 0.0000e+00 - val_loss: 9.1135e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 23/100\n",
      "904/904 [==============================] - 0s 157us/step - loss: 1.5282e-04 - accuracy: 0.0000e+00 - val_loss: 8.6990e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 24/100\n",
      "904/904 [==============================] - 0s 162us/step - loss: 1.5245e-04 - accuracy: 0.0000e+00 - val_loss: 8.3171e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 25/100\n",
      "904/904 [==============================] - 0s 193us/step - loss: 1.5213e-04 - accuracy: 0.0000e+00 - val_loss: 8.5297e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 26/100\n",
      "904/904 [==============================] - 0s 128us/step - loss: 1.5205e-04 - accuracy: 0.0000e+00 - val_loss: 8.2074e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 27/100\n",
      "904/904 [==============================] - 0s 115us/step - loss: 1.5263e-04 - accuracy: 0.0000e+00 - val_loss: 9.7985e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 28/100\n",
      "904/904 [==============================] - 0s 122us/step - loss: 1.5922e-04 - accuracy: 0.0000e+00 - val_loss: 8.3647e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 29/100\n",
      "904/904 [==============================] - 0s 119us/step - loss: 1.4654e-04 - accuracy: 0.0000e+00 - val_loss: 8.1103e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 30/100\n",
      "904/904 [==============================] - 0s 137us/step - loss: 1.4638e-04 - accuracy: 0.0000e+00 - val_loss: 8.3256e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 31/100\n",
      "904/904 [==============================] - 0s 141us/step - loss: 1.4436e-04 - accuracy: 0.0000e+00 - val_loss: 8.3477e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 32/100\n",
      "904/904 [==============================] - 0s 148us/step - loss: 1.4356e-04 - accuracy: 0.0000e+00 - val_loss: 8.0356e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 33/100\n",
      "904/904 [==============================] - 0s 177us/step - loss: 1.4656e-04 - accuracy: 0.0000e+00 - val_loss: 7.9044e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 34/100\n",
      "904/904 [==============================] - 0s 205us/step - loss: 1.4136e-04 - accuracy: 0.0000e+00 - val_loss: 8.3093e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 35/100\n",
      "904/904 [==============================] - 0s 142us/step - loss: 1.4025e-04 - accuracy: 0.0000e+00 - val_loss: 7.9817e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 36/100\n",
      "904/904 [==============================] - 0s 158us/step - loss: 1.4092e-04 - accuracy: 0.0000e+00 - val_loss: 7.2356e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 37/100\n",
      "904/904 [==============================] - 0s 124us/step - loss: 1.3711e-04 - accuracy: 0.0000e+00 - val_loss: 7.9709e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 38/100\n",
      "904/904 [==============================] - 0s 141us/step - loss: 1.3508e-04 - accuracy: 0.0000e+00 - val_loss: 8.3019e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 39/100\n",
      "904/904 [==============================] - 0s 122us/step - loss: 1.3957e-04 - accuracy: 0.0000e+00 - val_loss: 7.7031e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 40/100\n",
      "904/904 [==============================] - 0s 140us/step - loss: 1.3316e-04 - accuracy: 0.0000e+00 - val_loss: 8.3808e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 41/100\n",
      "904/904 [==============================] - 0s 158us/step - loss: 1.3349e-04 - accuracy: 0.0000e+00 - val_loss: 7.4802e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 42/100\n",
      "904/904 [==============================] - 0s 215us/step - loss: 1.2856e-04 - accuracy: 0.0000e+00 - val_loss: 7.1043e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 43/100\n",
      "904/904 [==============================] - 0s 142us/step - loss: 1.2870e-04 - accuracy: 0.0000e+00 - val_loss: 7.3412e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 44/100\n",
      "904/904 [==============================] - 0s 163us/step - loss: 1.2312e-04 - accuracy: 0.0000e+00 - val_loss: 6.6367e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 45/100\n",
      "904/904 [==============================] - 0s 148us/step - loss: 1.2373e-04 - accuracy: 0.0000e+00 - val_loss: 7.0523e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 46/100\n",
      "904/904 [==============================] - 0s 162us/step - loss: 1.2342e-04 - accuracy: 0.0000e+00 - val_loss: 6.9989e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 47/100\n",
      "904/904 [==============================] - 0s 154us/step - loss: 1.2635e-04 - accuracy: 0.0000e+00 - val_loss: 6.4078e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 48/100\n",
      "904/904 [==============================] - 0s 159us/step - loss: 1.2196e-04 - accuracy: 0.0000e+00 - val_loss: 7.6946e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 49/100\n",
      "904/904 [==============================] - 0s 159us/step - loss: 1.2548e-04 - accuracy: 0.0000e+00 - val_loss: 6.5659e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 50/100\n",
      "904/904 [==============================] - 0s 160us/step - loss: 1.0978e-04 - accuracy: 0.0000e+00 - val_loss: 6.0362e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 51/100\n",
      "904/904 [==============================] - 0s 213us/step - loss: 1.1238e-04 - accuracy: 0.0000e+00 - val_loss: 5.9359e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 52/100\n",
      "904/904 [==============================] - 0s 162us/step - loss: 1.0496e-04 - accuracy: 0.0000e+00 - val_loss: 5.8242e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 53/100\n",
      "904/904 [==============================] - 0s 151us/step - loss: 1.0564e-04 - accuracy: 0.0000e+00 - val_loss: 6.0985e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 54/100\n",
      "904/904 [==============================] - 0s 135us/step - loss: 1.0663e-04 - accuracy: 0.0000e+00 - val_loss: 5.7487e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 55/100\n",
      "904/904 [==============================] - 0s 129us/step - loss: 1.0155e-04 - accuracy: 0.0000e+00 - val_loss: 5.4153e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 56/100\n",
      "904/904 [==============================] - 0s 126us/step - loss: 1.0290e-04 - accuracy: 0.0000e+00 - val_loss: 5.1176e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 57/100\n",
      "904/904 [==============================] - 0s 126us/step - loss: 1.0146e-04 - accuracy: 0.0000e+00 - val_loss: 5.0873e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 58/100\n",
      "904/904 [==============================] - 0s 168us/step - loss: 9.6245e-05 - accuracy: 0.0000e+00 - val_loss: 5.1846e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 59/100\n",
      "904/904 [==============================] - 0s 150us/step - loss: 9.1174e-05 - accuracy: 0.0000e+00 - val_loss: 4.9989e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 60/100\n",
      "904/904 [==============================] - 0s 151us/step - loss: 8.9279e-05 - accuracy: 0.0000e+00 - val_loss: 5.1975e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 61/100\n",
      "904/904 [==============================] - 0s 167us/step - loss: 8.9389e-05 - accuracy: 0.0000e+00 - val_loss: 4.8926e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 62/100\n",
      "904/904 [==============================] - 0s 163us/step - loss: 8.8930e-05 - accuracy: 0.0000e+00 - val_loss: 4.1685e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 63/100\n",
      "904/904 [==============================] - 0s 149us/step - loss: 8.5549e-05 - accuracy: 0.0000e+00 - val_loss: 4.6223e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 64/100\n",
      "904/904 [==============================] - 0s 157us/step - loss: 8.6261e-05 - accuracy: 0.0000e+00 - val_loss: 4.2263e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 65/100\n",
      "904/904 [==============================] - 0s 200us/step - loss: 7.6493e-05 - accuracy: 0.0000e+00 - val_loss: 4.0430e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 66/100\n",
      "904/904 [==============================] - 0s 167us/step - loss: 7.3701e-05 - accuracy: 0.0000e+00 - val_loss: 3.9726e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 67/100\n",
      "904/904 [==============================] - 0s 138us/step - loss: 7.4448e-05 - accuracy: 0.0000e+00 - val_loss: 3.8598e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 68/100\n",
      "904/904 [==============================] - 0s 142us/step - loss: 7.4029e-05 - accuracy: 0.0000e+00 - val_loss: 3.5036e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 69/100\n",
      "904/904 [==============================] - 0s 134us/step - loss: 6.8947e-05 - accuracy: 0.0000e+00 - val_loss: 3.3471e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 70/100\n",
      "904/904 [==============================] - 0s 162us/step - loss: 7.7165e-05 - accuracy: 0.0000e+00 - val_loss: 3.1898e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 71/100\n",
      "904/904 [==============================] - 0s 141us/step - loss: 7.0946e-05 - accuracy: 0.0000e+00 - val_loss: 3.1929e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 72/100\n",
      "904/904 [==============================] - 0s 122us/step - loss: 7.5978e-05 - accuracy: 0.0000e+00 - val_loss: 3.9519e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 73/100\n",
      "904/904 [==============================] - 0s 136us/step - loss: 6.8316e-05 - accuracy: 0.0000e+00 - val_loss: 2.8109e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 74/100\n",
      "904/904 [==============================] - 0s 118us/step - loss: 5.6323e-05 - accuracy: 0.0000e+00 - val_loss: 2.7028e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 75/100\n",
      "904/904 [==============================] - 0s 99us/step - loss: 5.4272e-05 - accuracy: 0.0000e+00 - val_loss: 3.0587e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 76/100\n",
      "904/904 [==============================] - 0s 125us/step - loss: 5.2615e-05 - accuracy: 0.0000e+00 - val_loss: 2.7522e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 77/100\n",
      "904/904 [==============================] - 0s 125us/step - loss: 4.9177e-05 - accuracy: 0.0000e+00 - val_loss: 2.7666e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 78/100\n",
      "904/904 [==============================] - 0s 116us/step - loss: 4.5835e-05 - accuracy: 0.0000e+00 - val_loss: 2.4373e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 79/100\n",
      "904/904 [==============================] - 0s 117us/step - loss: 4.5714e-05 - accuracy: 0.0000e+00 - val_loss: 2.3210e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 80/100\n",
      "904/904 [==============================] - 0s 111us/step - loss: 4.1916e-05 - accuracy: 0.0000e+00 - val_loss: 2.1263e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 81/100\n",
      "904/904 [==============================] - 0s 114us/step - loss: 4.3305e-05 - accuracy: 0.0000e+00 - val_loss: 2.3462e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 82/100\n",
      "904/904 [==============================] - 0s 141us/step - loss: 4.1967e-05 - accuracy: 0.0000e+00 - val_loss: 2.0353e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 83/100\n",
      "904/904 [==============================] - 0s 162us/step - loss: 3.9731e-05 - accuracy: 0.0000e+00 - val_loss: 1.8040e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 84/100\n",
      "904/904 [==============================] - 0s 156us/step - loss: 3.8103e-05 - accuracy: 0.0000e+00 - val_loss: 1.7960e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 85/100\n",
      "904/904 [==============================] - 0s 131us/step - loss: 4.3123e-05 - accuracy: 0.0000e+00 - val_loss: 1.6485e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 86/100\n",
      "904/904 [==============================] - 0s 158us/step - loss: 3.0817e-05 - accuracy: 0.0000e+00 - val_loss: 1.5910e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 87/100\n",
      "904/904 [==============================] - 0s 174us/step - loss: 3.0803e-05 - accuracy: 0.0000e+00 - val_loss: 1.5114e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 88/100\n",
      "904/904 [==============================] - 0s 245us/step - loss: 3.2859e-05 - accuracy: 0.0000e+00 - val_loss: 1.6271e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 89/100\n",
      "904/904 [==============================] - 0s 200us/step - loss: 2.8488e-05 - accuracy: 0.0000e+00 - val_loss: 1.4269e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 90/100\n",
      "904/904 [==============================] - 0s 212us/step - loss: 3.1739e-05 - accuracy: 0.0000e+00 - val_loss: 1.3239e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 91/100\n",
      "904/904 [==============================] - 0s 181us/step - loss: 2.7265e-05 - accuracy: 0.0000e+00 - val_loss: 1.2604e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 92/100\n",
      "904/904 [==============================] - 0s 134us/step - loss: 3.4941e-05 - accuracy: 0.0000e+00 - val_loss: 1.3312e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 93/100\n",
      "904/904 [==============================] - 0s 181us/step - loss: 2.8824e-05 - accuracy: 0.0000e+00 - val_loss: 1.1787e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 94/100\n",
      "904/904 [==============================] - 0s 245us/step - loss: 2.7202e-05 - accuracy: 0.0000e+00 - val_loss: 1.1204e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 95/100\n",
      "904/904 [==============================] - 0s 171us/step - loss: 2.1336e-05 - accuracy: 0.0000e+00 - val_loss: 1.1594e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 96/100\n",
      "904/904 [==============================] - 0s 167us/step - loss: 2.4261e-05 - accuracy: 0.0000e+00 - val_loss: 1.0642e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 97/100\n",
      "904/904 [==============================] - 0s 163us/step - loss: 2.4914e-05 - accuracy: 0.0000e+00 - val_loss: 1.1711e-04 - val_accuracy: 0.0000e+00\n",
      "Epoch 98/100\n",
      "904/904 [==============================] - 0s 158us/step - loss: 2.2342e-05 - accuracy: 0.0000e+00 - val_loss: 9.7970e-05 - val_accuracy: 0.0000e+00\n",
      "Epoch 99/100\n",
      "904/904 [==============================] - 0s 200us/step - loss: 1.9858e-05 - accuracy: 0.0000e+00 - val_loss: 9.4534e-05 - val_accuracy: 0.0000e+00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 100/100\n",
      "904/904 [==============================] - 0s 218us/step - loss: 2.6779e-05 - accuracy: 0.0000e+00 - val_loss: 9.1993e-05 - val_accuracy: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "如果直接加载模型，则不需要运行这一段训练\n",
    "'''\n",
    "#神经网络训练及结果\n",
    "model.fit(X_train, y_train, epochs =epoch, batch_size = batch_size,verbose=1,validation_split=0.1) #训练模型nb_epoch次\n",
    "model.save(save_model)#保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 24020,
     "status": "ok",
     "timestamp": 1597492093480,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "xmFQUb2potjw",
    "outputId": "6b95de27-3802-4c45-f389-db605f42616a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集上的MAE/RMSE/MAPE/涨跌准确率\n",
      "7.075808457711451\n",
      "9.633713830814632\n",
      "0.3073237252602772\n",
      "0.5438247011952191\n"
     ]
    }
   ],
   "source": [
    "#加载模型\n",
    "from keras.models import load_model\n",
    "model=load_model(save_model)  \n",
    "#模型进行检验\n",
    "def mape(y_true, y_pred):\n",
    "    return np.mean(np.abs((y_pred - y_true) / y_true)) * 100\n",
    "def up_down_accuracy(y_true, y_pred):\n",
    "    y_var_test=y_true[1:]-y_true[:len(y_true)-1]#实际涨跌\n",
    "    y_var_predict=y_pred[1:]-y_pred[:len(y_pred)-1]#原始涨跌\n",
    "    txt=np.zeros(len(y_var_test))\n",
    "    for i in range(len(y_var_test-1)):#计算数量\n",
    "        txt[i]=np.sign(y_var_test[i])==np.sign(y_var_predict[i])\n",
    "    result=sum(txt)/len(txt)\n",
    "    return result\n",
    "#在训练集上的拟合结果\n",
    "y_train_predict=model.predict(X_train)[:,0]*scale#反归一化\n",
    "y_train=y_train*scale\n",
    "#展示在训练集上的表现 \n",
    "draw=pd.concat([pd.DataFrame(y_train),pd.DataFrame(y_train_predict)],axis=1)\n",
    "draw.iloc[:,0].plot(figsize=(12,6))\n",
    "draw.iloc[:,1].plot(figsize=(12,6))\n",
    "plt.legend(('real', 'predict'),fontsize='15')\n",
    "plt.title(\"Train Data\",fontsize='30') #添加标题\n",
    "plt.show()\n",
    "#输出结果\n",
    "print('训练集上的MAE/RMSE/MAPE/涨跌准确率')\n",
    "print(mean_absolute_error(y_train_predict, y_train))\n",
    "print(np.sqrt(mean_squared_error(y_train_predict, y_train)))\n",
    "print(mape(y_train_predict, y_train) )\n",
    "print(up_down_accuracy(y_train_predict,y_train))\n",
    "model.save('lstm.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 24016,
     "status": "ok",
     "timestamp": 1597492093481,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "puevKtbiotj2",
    "outputId": "e857e715-5379-4301-9d78-8d1db85e704b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集上的MAE/RMSE/MAPE/涨跌准确率\n",
      "7.15327398838142\n",
      "8.213546315150268\n",
      "0.32180529290167104\n",
      "0.47413793103448276\n"
     ]
    }
   ],
   "source": [
    "#在测试集上的预测\n",
    "y_test_predict=model.predict(X_test)[:,0]*scale\n",
    "y_test=y_test*scale\n",
    "#展示在测试集上的表现 \n",
    "draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_test_predict)],axis=1);\n",
    "draw.iloc[:,0].plot(figsize=(12,6))\n",
    "draw.iloc[:,1].plot(figsize=(12,6))\n",
    "plt.legend(('real', 'predict'),loc='upper right',fontsize='15')\n",
    "plt.title(\"Test Data\",fontsize='30') #添加标题\n",
    "plt.show()\n",
    "print('测试集上的MAE/RMSE/MAPE/涨跌准确率')\n",
    "print(mean_absolute_error(y_test_predict, y_test))\n",
    "print(np.sqrt(mean_squared_error(y_test_predict, y_test)))\n",
    "print(mape(y_test_predict,  y_test) )\n",
    "print(up_down_accuracy(y_test_predict,y_test))\n",
    "#储存文件\n",
    "(pd.DataFrame(y_test_predict)).to_csv(save_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 25221,
     "status": "ok",
     "timestamp": 1597492094690,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "ankBBnAQotj4",
    "outputId": "a49a451f-23d0-4935-ccdc-e940bf573522"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画完整图\n",
    "observed=np.hstack((y_train,y_test)) \n",
    "evaluated=y_train_predict\n",
    "predicted=y_test_predict\n",
    "observed_times=np.arange(len(y_train)+len(y_test))\n",
    "evaluated_times=np.arange(len(y_train))\n",
    "predicted_times=np.arange(len(y_train),len(y_train)+len(y_test))\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.axvline(len(y_train), linestyle=\"dotted\", linewidth=5, color='r')\n",
    "observed_lines = plt.plot(observed_times, observed, label=\"observation\", color=\"k\")\n",
    "evaluated_lines = plt.plot(evaluated_times, evaluated, label=\"evaluation\", color=\"g\")\n",
    "predicted_lines = plt.plot(predicted_times, predicted, label=\"prediction\", color=\"r\")\n",
    "\n",
    "plt.legend(handles=[observed_lines[0], evaluated_lines[0], predicted_lines[0]],\n",
    "         loc=\"upper left\")\n",
    "#为训练，验证，预测数据分别创建图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 25217,
     "status": "ok",
     "timestamp": 1597492094690,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "Dx57DzoPotj7"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 25213,
     "status": "ok",
     "timestamp": 1597492094691,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "mrFTh7rZotj9"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 25211,
     "status": "ok",
     "timestamp": 1597492094691,
     "user": {
      "displayName": "Expss Xu",
      "photoUrl": "",
      "userId": "17480852382145563764"
     },
     "user_tz": -480
    },
    "id": "7kbUZXklotkA"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "colab": {
   "collapsed_sections": [],
   "name": "lstm.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
